Mohammad Navid Shahsavari
Papers
2
Total Citations
7
H-Index
2
About
Mohammad Navid Shahsavari is a robotics researcher whose work focuses on enabling humanoid robots to interact more effectively with dynamic environments, particularly through precise ball trajectory prediction. His key research areas include humanoid robotics, motion planning, and physics-based modeling. Shahsavari’s major contributions lie in developing predictive algorithms that account for real-world physical constraints, such as friction, to improve a robot’s ability to anticipate and respond to moving objects. In his 2019 study, "Prediction of a Ball Trajectory for the Humanoid Robots: A Friction-Based Study," he introduced a friction-aware model that significantly enhanced prediction accuracy, earning 4 citations for its foundational approach. He further advanced this work in 2022 with "Ball Path Prediction for Humanoid Robots: Combination of k-NN Regression and Autoregression Methods," where he integrated machine learning techniques to refine real-time predictions, garnering 3 citations. Though early in his career, Shahsavari’s research bridges classical physics and modern data-driven methods, offering practical solutions for humanoid robots in sports, search-and-rescue, and human-robot interaction. His work stands out for its focus on robustness under uncertain conditions, making it a valuable resource for students and researchers interested in the intersection of robotics, control systems, and applied machine learning.
Research Focus
Key Achievements
Top Papers
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